Bootstrap Aggregating (or bagging for short) is a model averaging technique designed to improve the stability and performance of a user-specified base estimator by training a number of them on a unique bootstrapped training set sampled at random with replacement. Bagging works especially well with estimators that tend to have high prediction variance by reducing the variance through averaging.
Data Type Compatibility: Depends on base learner
|1||base||Learner||The base learner.|
|2||estimators||10||int||The number of base learners to train in the ensemble.|
|3||ratio||0.5||float||The ratio of samples from the training set to randomly subsample to train each base learner.|
use Rubix\ML\BootstrapAggregator; use Rubix\ML\Regressors\RegressionTree; $estimator = new BootstrapAggregator(new RegressionTree(10), 300, 0.2);
This meta estimator does not have any additional methods.
- L. Breiman. (1996). Bagging Predictors.